Application of Spectral Statistics to Spectral Texture Discrimination

International audienceHyperspectral imaging has continued to be exploited in various fields for its offer of gain in accuracy, despite the cost and complexity of its acquisition. However, accuracy must be maintained in all processing chain for the potential to be optimally exploited. A spectrum is a continuous function over the wavelengths and it must be processed as such. Taking that into consideration, in this article, a statistical processing of hyperspectral images is proposed. The statistics is based on the Kullback-Leibler pseudo-divergence measure, which incorporates the mathematical definition of a spectrum as a continuous function. The interest of these statistics is then demonstrated through a task of spectral texture discrimination